We learnt that we can use read.csv() function to import data from files in comma separated values (CSV) format. A data table can reside in a text file where the cells inside the table are separated by blank characters. If your data uses another character to separate the fields, not a comma, R also has the more general read.table() function.

So if your separator is a tab, for instance, this would work:

mydata <- read.table("filename.txt", sep="\t", header=TRUE)

The command above also indicates there's a header row in the file with header=TRUE.

If, say, your separator is a character such as | you would change the separator part of the command to sep="|".

Categories or values?

Because of R's roots as a statistical tool, when you import non-numerical data, R may assume that character strings are statistical factors -- things like "poor," "average" and "good" -- or "success" and "failure."

But your text columns may not be categories that you want to group and measure, just names of companies or employees. If you don't want your text data to be read in as factors, add stringsAsFactor=FALSE to read.table, like this:

mydata <- read.table("filename.txt", sep="\t", header=TRUE, stringsAsFactor=FALSE)